If 1998 was the year of enterprise resource planning (ERP) applications, and 2000 the year of customer relationship management (CRM), 2001 or 2002 could be the year that Business Intelligence (BI)/data warehouse moves into the mainstream of enterprise applications. According to an exclusive analysis of data provided by Survey.com of the companies actively involved with data warehouse projects, more than 40 percent are currently in the planning process, while 12 percent have completed their solutions, as Graph 1 shows.
Although the concept of a data warehouse—a unified, integrated repository for enterprise data—first got attention in the late 1980s, reality has never caught up to the concept's potential. The idea is simple. If companies can synthesize the data collected throughout the organization in databases structured for analytical purposes, those companies can mine the databases to gain a better understanding of the enterprise as a whole. Best practices can be identified and expanded. Inefficiencies can be eliminated. In short, the wealth of information can be analyzed to improve business processes.
Although that kind of application sounds like a no-brainer for information-rich corporations, building and deploying data warehouses has proven to be a challenge. As Graph 2 indicates, only one quarter of the companies with ongoing BI/data warehouse projects have actually completed one.
Data warehouse projects face manifold obstacles. The first is integrating new and old information systems. Throughout the 1990s, relational databases from Microsoft and Oracle have increasingly supplemented or replaced mainframe-based systems as the primary repositories for corporate information. Consequently, data warehouse projects often involve integrating older systems with newer information technology—no easy task.
Second, companies generate information faster. ERP, CRM, supply chain management (SCM) software, sales force automation (SFA) software, and virtually every other business-process automation package depends on the improved capture, storage, communication, and retrieval of information. Obsolescence looms for data warehouse projects that aren't implemented expeditiously and completed promptly.
An effective data warehouse must contain information accessible to a range of the organization's decision makers. Such broad access to information for analysis provides managers with a single, synthesized, coherent view of operations. On the other hand, managing widespread access and security demands complex juggling.
In the final analysis, many companies that jumped on the data-warehouse bandwagon early faced disappointing results. Of those companies with completed projects, about 33 percent reported that their BI/data warehouse projects fell short of expectations, failed, or were abandoned. Only 7 percent said that their BI/data warehouse projects far exceeded their expectations (see Graph 3).
Despite that rather dismal record, a wider range of companies now seem willing to consider BI/data warehouse projects. In the past, primarily cutting-edge IT shops conducted BI/data warehouse projects; now, companies that see themselves in the mainstream of using new technology plan to start BI/data warehouse projects (see Graph 4).
BI/data warehouse isn't for the faint of heart, but companies can no longer sit safely on the sidelines, either. Firms that use data to improve business processes will gain a significant competitive edge. Early pioneers that successfully completed BI/data warehouse projects have enjoyed clearly measurable returns on their investments. As BI/data warehouse technology moves into the mainstream, its use will help separate the corporate winners from losers.
